A typical flat sheet industry manufactures raw rolls and/or sheets that consist of, for example, steel, paper, aluminum, and dry film products. The raw rolls (sheets) have varying widths, diameters (or length), quantity, and quality. The width and diameter (or length) of these raw rolls and/or sheets depend on the specifications of the machines that manufacture them. On the other hand, customers often order product rolls and/or sheets that have smaller dimensions that cannot be directly satisfied by the raw rolls and/or sheets produced by flat sheet manufacturers. Therefore, raw rolls and/or sheets frequently must be cut and/or trimmed into product rolls and/or product sheets that satisfy customer demand.
The process of cutting the larger sized raw rolls and/or sheets into smaller product rolls and/or sheets as specified by customer orders is typically referred to as trimming, cutting, and/or charting, depending on the type of flat sheet industry. However, for convenience only, the term charting will be used herein to mean trimming, cutting, or charting.
Charting is carried out using a set of specific equipment (e.g., winder, rewinder, sheeter, trimmer, cutter, etc) in various flat sheet industries. The equipment is generally referred to as secondary processing equipment and the process of charting is generally referred to as secondary processing. Secondary processing could be implemented in either one step or multiple steps depending on the specific dimensions that need to be charted. Multiple steps are typically required due to inherent limitations of the various secondary processing equipment, in terms of its capability in handling different dimensions.
Charting is typically carried out multiple times a day (e.g., a separate charting for each shift or separate charting for a group of customer orders referred to as a charting batch, etc). The charting process primarily includes the determination of the necessary charting patterns according to which large sized raw rolls and/or sheets are processed on secondary processing equipment to fulfill a set of customer orders. The attributes of the customer orders are typically width, diameter (or length), ordered quantity (within certain tolerances), product type and delivery date.
The conventional approach to achieve better charting yields relies on using standard sizes along with customer order dimensions while minimizing the quantity of raw rolls/sheets required to fulfill customer orders. This approach has the inherent problem of delivering a quantity that is less than ‘target customer order quantity’ but that is within a lower tolerance on ‘target customer order quantity’. The changing customer base in today's competitive industrial world results in irregular demand sizes with no or very few standard sizes; this limits the conventional approach in finding better charting yields. Furthermore, as standard sizes do not have associated delivery dates, the conventional approach invariably affects the production schedule because of the time consumed in producing standard sizes.
The majority of prior art charting optimization approaches focus on either minimization of charting loss or minimization of the number of raw rolls (or sheet quantity) required (either by manufacturing or from available inventory) to fulfill the customer orders. For example, Gilmore and Gomory, (1961), “A Linear Programming Approach to Cutting Stock Problem,” Operations Research, 9, 849-859, considers minimization of raw rolls to fulfill customer orders as only one business objective for single stage/level charting and ignores other business objectives. The few multi stage/level charting approaches that are described, such as in U.S. Pat. No. 7,987,016 to Karhu, focus on other business objectives like effective utilization of the secondary processing equipment without giving sufficient priority to charting loss minimization and hence do not guarantee the optimal charting yield. A few other charting approaches consider the standard sizes to improve the charting yield but they fail to limit the use of standard sizes resulting in production of large quantity for them and thereby affect the schedule delivery dates for confirmed customer orders. See, for example, Chauhan et al., (2008), “Roll Assortment Optimization in Paper Mill—An Integer Programming Approach,” Computer & Operation Research, and U.S. Pat. No. 6,745,099 to Hoffman. Furthermore, the standard sizes considered in such approaches often are not part of any future customer orders resulting in increased finished product inventory and locked capital.
In addition, the currently available charting solution approaches, such as those described in U.S. Pat. No. 7,610,114 to Kapadi et al. and EP1956456 B1, do not provide information on bounds (upper and/or lower) of various business objectives even though they consider all the business goals as part of the objective function. From the charting solutions obtained using these currently available approaches it is difficult to judge the deterioration (if any) in charting yield and improvement (if any) in other business objectives compared with the values obtained for all business objectives when only charting loss minimization approach is used. In summary, these approaches do not demonstrate to users the explicit trade-off between various business objectives for a charting batch or run. Furthermore, these approaches completely explore neither the flexibility available in charting activity nor multiple solutions having same charting loss to improve upon all the business goals of the charting process. In other words, these approaches do not provide any insight to enable users to prioritize the business goals as per their needs separately for each charting batch or run and hence fall short in achieving a delicate balance between all the business goals to improve the overall charting operation and not only the charting yield.
With changing economic conditions, the concept of warehouse is getting thinner and the industry-operating model is fast moving from make-to-stock to make-to-orders concept. Therefore, the business goals for a charting process are now a balanced combination of the following factors: (1) Minimization of quantity loss during charting. (2) Maximum utilization of available inventory of raw or semi-processed rolls/sheets. (3) Minimization of the number of raw rolls (or raw sheet quantity) required to be manufactured to fulfill a set of customer orders (from a batch). (4) Minimization of deviation of ‘quantity delivered’ from ‘target order quantity’ for each “must make” customer order, which is high priority order that must be fulfilled completely within specific quantity tolerance limits. (5) Minimization of quantity produced for optional orders. The optional orders are either standard size orders (for which there is regular flow of customer orders) or customer orders with future delivery date. The production of quantity for optional orders results in handling of finished product inventory at manufacturing site and lock-in of capital. (6) Maximization of throughput of all secondary processing equipments. And (7) Minimization of setup required on secondary processing equipments.
Although the primary goal of charting process is always to minimize charting/trimming loss, the other business objectives carry relevant importance and cannot be ignored. Currently available charting solutions either provide maximum priority to charting loss minimization or consider a user-defined combination of all these business goals. In the former approach charting process is not efficient though charting yield is best while in the latter approach the primary objective of trimming/charting loss minimization is not adequately considered. Currently available charting solutions when implemented addresses a few (or all) of the above listed business goals along with charting loss minimization yield; the results keep the user unaware with respect to deviation from optimal charting loss (obtained in absence of other business goals) and improvement (if any) in other business goals against pure charting loss minimization approach. In absence of the said insight into the charting result, currently available charting solutions do not allow users to prioritize the business goals as per their needs for each charting batch or run. Hence, the user of currently available charting solutions finds it difficult to achieve a delicate balance between all the business goals to improve the overall charting operation and not just the charting yield.
Finally, currently available charting solutions completely fail to explore the flexibility available in charting activity and multiple solutions having same charting loss to improve upon all the business goals of the charting process. It is important to understand that multiple solutions providing the same percentage of charting loss can perform differently with respect to other business objectives or goals. For example, a solution needs fewer number of unique charting patterns as compared with other solutions having the same percentage charting loss; this in turn results in minimum setup time on secondary processing units. Other solutions having the same percentage trim loss might be producing a minimum quantity for optional orders. Or a slight deterioration in charting percentage yield (which is 100 minus charting percentage loss) helps improve the available inventory (of raw or semi-processed rolls/sheets) utilization (mainly in the case of inventory nearing its expiry) and/or throughput of secondary processing units along with minimum setup time. When such deterioration (if any) in charting yield is acceptable to a user against the improvement in other business objectives, then the new result becomes the best solution for a given charting batch or run.